GeneDx, LLC, Gaithersburg, Maryland, USA.
n-Lorem Foundation, Carlsbad, California, USA.
Am J Med Genet C Semin Med Genet. 2023 Sep;193(3):e32059. doi: 10.1002/ajmg.c.32059. Epub 2023 Aug 3.
Facial analysis technology in rare diseases has the potential to shorten the diagnostic odyssey by providing physicians with a valuable diagnostic tool. Given that most clinical genetic resources focus on populations of European descent, we compare craniofacial features in genetic syndromes across different populations and review how machine learning algorithms perform on diagnosing genetic syndromes in geographically and ethnically diverse populations. We also discuss the value of populations from ancestrally diverse backgrounds in the training set of machine learning algorithms. Finally, this review demonstrates that across diverse population groups, machine learning models have outstanding accuracy as supported by the area under the curve values greater than 0.9. Artificial intelligence is only in its infancy in the diagnosis of rare disease in diverse populations and will become more accurate as larger and more diverse training sets, including a wider spectrum of ages, particularly infants, are studied.
罕见病面部分析技术有可能通过为医生提供有价值的诊断工具来缩短诊断的探索过程。鉴于大多数临床遗传资源都集中在欧洲血统的人群中，我们比较了不同人群中遗传综合征的颅面特征，并回顾了机器学习算法在诊断地理和种族多样化人群中的遗传综合征时的表现。我们还讨论了在机器学习算法的训练集中，来自具有不同祖先背景的人群的价值。最后，本综述表明，在不同的人群中，机器学习模型具有出色的准确性，曲线下面积值大于 0.9 支持这一结论。在不同人群中诊断罕见病方面，人工智能仍处于起步阶段，随着更大、更多样化的训练集的研究，包括更广泛的年龄范围，特别是婴儿，人工智能将变得更加准确。
